Personalized Predictive Models for Symptomatic COVID-19 Patients Using Basic Preconditions:Hospitalizations, Mortality, and the Need for an ICU or Ventilator

Abstract

Background: The rapid global spread of the virus SARS-CoV-2 has provoked a spike in demand for hospital care. Hospital systems across the world have been over-extended, including in Northern Italy, Ecuador, and New York City, and many other systems face similar challenges. As a result, decisions on how to best allocate very limited medical resources have come to the forefront. Specifically, under consideration are decisions on who to test, who to admit into hospitals, who to treat in an Intensive Care Unit (ICU), and who to support with a ventilator. Given today's ability to gather, share, analyze and process data, personalized predictive models based on demographics and information regarding prior conditions can be used to (1) help decision-makers allocate limited resources, when needed, (2) advise individuals how to better protect themselves given their risk profile, (3) differentiate social distancing guidelines based on risk, and (4) prioritize vaccinations once a vaccine becomes available. Objective: To develop personalized models that predict the following events: (1) hospitalization, (2) mortality, (3) need for ICU, and (4) need for a ventilator. To predict hospitalization, it is assumed that one has access to a patient's basic preconditions, which can be easily gathered without the need to be at a hospital. For the remaining models, different versions developed include different sets of a patient's features, with some including information on how the disease is progressing (e.g., diagnosis of pneumonia). Materials and Methods: Data from a publicly available repository, updated daily, containing information from approximately 91,000 patients in Mexico were used. The data for each patient include demographics, prior medical conditions, SARS-CoV-2 test results, hospitalization, mortality and whether a patient has developed pneumonia or not. Several classification methods were applied, including robust versions of logistic regression, and support vector machines, as well as random forests and gradient boosted decision trees. Results: Interpretable methods (logistic regression and support vector machines) perform just as well as more complex models in terms of accuracy and detection rates, with the additional benefit of elucidating variables on which the predictions are based. Classification accuracies reached 61%, 76%, 83%, and 84% for predicting hospitalization, mortality, need for ICU and need for a ventilator, respectively. The analysis reveals the most important preconditions for making the predictions. For the four models derived, these are: (1) for hospitalization: age, gender, chronic renal insufficiency, diabetes, immunosuppression; (2) for mortality: age, SARS-CoV-2 test status, immunosuppression and pregnancy; (3) for ICU need: development of pneumonia (if available), cardiovascular disease, asthma, and SARS-CoV-2 test status; and (4) for ventilator need: ICU and pneumonia (if available), age, gender, cardiovascular disease, obesity, pregnancy, and SARS-CoV-2 test result.

Acute kidney injury in COVID‐19: Identification of risk factors and potential biomarkers of disease in a large UK cohort

“…In our hospital, there was a significant increase in the number of patients with AKI of all stages in the COVID‐19 positive population when compared to the same time period in 2019. The rate of AKI in the COVID‐19 positive population is comparable to other studies 8,22,23 although the incidence of AKI in European and North American studies appears to be greater than those conducted in China 6 . Patients also suffered similarly poor outcomes such as admission to ICU for organ support, increased length of stay and death.…”

Individuals with obesity and COVID‐19: A global perspective on the epidemiology and biological relationships

“…We identified 19 studies that examined the relationship and included them in this analysis 1,28,38,40,44,47–58 . Table S4 presents the results 1,45,47,48,59,60 ; all showed a significantly higher prevalence of individuals with obesity among hospitalized patients than among patients not hospitalized or the general population. The pooled OR was 2.13 (95% CI, 1.74–2.60; p < 0.0001) (Figure 4).…”

Outcomes of coronavirus 2019 infection in patients with chronic kidney disease: a systematic review and meta-analysis

“…After screening, 27 articles were included for further analysis. 20 – 46 The process of selection of the included studies is shown in Figure 1 .…”

“…In our meta-analysis, information on the critical or severe condition was available in 11 studies. 23 , 35 – 44 Of the 21,387 patients with COVID-19 infection, 5411 patients suffered from critically ill condition during the study period: 507 patients in the CKD group and 4904 patients in the non-CKD group. The heterogeneity test showed high heterogeneity among these studies ( I 2 = 90%), and a random-effects model was used.…”